WEBVTT - Under-Representation in Robotics and AI is Solvable

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<v Speaker 1>Pushkin. This is solvable. I'm Ronald Young Junior. Every single

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<v Speaker 1>instance where something has transformed our society for good, there's

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<v Speaker 1>always been this fear. Some of the greatest advancements in

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<v Speaker 1>our society are directly linked to technological breakthroughs. Whether it

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<v Speaker 1>was the wheel, the printing press, or the microchip, the

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<v Speaker 1>world has been transformed from generation to generation. In many cases,

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<v Speaker 1>transformation was not greeted with acceptance and quick adoption, but

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<v Speaker 1>instead with anger, fear, and apprehension. And this is the

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<v Speaker 1>reception that has met artificial intelligence, that potential negative of

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<v Speaker 1>being reliant on this software, on AI, on robotics, that

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<v Speaker 1>the negatives are less than the post as it is.

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<v Speaker 1>But it's not just the fear of reliance on these technologies.

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<v Speaker 1>It's also the fear of the technology magnifying and multiplying

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<v Speaker 1>our worst two intendencies. There have been troubling instances where

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<v Speaker 1>bias and robotics and AI have been identified as a

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<v Speaker 1>potential disqualifying factor for their wide deployment. These systems have

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<v Speaker 1>some aspect of bias, time and time and time again,

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<v Speaker 1>they're still better than the human biases. Doctor Ayanna Howard

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<v Speaker 1>is the Dean of the College of engineering at the

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<v Speaker 1>Ohio State University and one of the few non white

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<v Speaker 1>male roboticists in the field. So I don't think that

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<v Speaker 1>these systems can ever get to zero bias, because there

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<v Speaker 1>will always be a group that the system has not

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<v Speaker 1>interacted with. It might be that it's perfect, as perfect

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<v Speaker 1>is perfect, and then there's an unknown community in South

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<v Speaker 1>Wales somewhere that had never interacted right, and now it

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<v Speaker 1>doesn't work with them. Doctor Howard founded a robotics company

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<v Speaker 1>called zy Robotics. They develop mobile therapies and educational products

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<v Speaker 1>for children with special needs. She firmly believes that the

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<v Speaker 1>proper use of AI and robotics is an invaluable asset

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<v Speaker 1>to humanity. Underrepresentation in robotics and AI is a solvable problem.

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<v Speaker 1>So when I was a kid, me and my sister

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<v Speaker 1>got left at home a lot during the summers, which

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<v Speaker 1>meant that we watched a lot of reruns on television,

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<v Speaker 1>and one of the things that we watched all the

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<v Speaker 1>time was The Biotic Woman, and I heard that that

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<v Speaker 1>was a favorite of yours as well. It was um

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<v Speaker 1>although I will tell you the Beyondic Woman when I

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<v Speaker 1>was young was current, so I'm sure you must have

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<v Speaker 1>saw the reruns, say but yeah, that was that was

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<v Speaker 1>my favorite, and I think what you know, I was

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<v Speaker 1>always into anything that was science fiction, everything from you know,

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<v Speaker 1>Battlestar Galactica, Star Wars. Do you mean the yeah, no, yeah,

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<v Speaker 1>the original, not the ones that they tried to remake

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<v Speaker 1>over and over and over again. Back then it was

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<v Speaker 1>all of it was like new right, it was like fascinating. Yeah.

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<v Speaker 1>So The Beondic Woman was this show where this this

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<v Speaker 1>woman was horribly mangled in like this this horrific car accident,

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<v Speaker 1>right like accident, it was a skydiving she would have died, right,

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<v Speaker 1>and instead the doctors took her and rebuilt her basically

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<v Speaker 1>added the Beiondic parts and she would go around saving

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<v Speaker 1>the world, which was was it was awesome, but she

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<v Speaker 1>was human, like she had a personality. That was the

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<v Speaker 1>nice thing about that. Yes, like she wasn't exactly a robot,

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<v Speaker 1>but she was like she was a robot. It was

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<v Speaker 1>actually if you think about it, there was only two

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<v Speaker 1>sci fi shows that showed women in like a positive

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<v Speaker 1>superhero light. One was wonder Woman and one was The

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<v Speaker 1>Roonic Woman. So like one of the earliest forms of

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<v Speaker 1>a superhero. I saw was the biotic was So I

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<v Speaker 1>appreciate that. But tell me a little bit, how about

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<v Speaker 1>how that watching that show kind of pique your interest

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<v Speaker 1>in STEM. Well, so when I was watching all of

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<v Speaker 1>these things, it also coincided with the time in middle

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<v Speaker 1>school where you have to define the rest of your life.

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<v Speaker 1>So they still do this, right, you have to write

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<v Speaker 1>these essays about what do you want to do when

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<v Speaker 1>you grow up? And so I wanted to build a

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<v Speaker 1>broonic women. So that's what I wanted to do. Now.

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<v Speaker 1>Of course, as my teacher said, well, building a bionic

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<v Speaker 1>woman is not actually a career, so you have to

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<v Speaker 1>think about, like what label do you want? And so

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<v Speaker 1>originally I thought I wanted to be a doctor because

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<v Speaker 1>that was who put her together. So what changed? I

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<v Speaker 1>took biology and we were dissecting frogs and I remember

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<v Speaker 1>we had to learn how to kill the frogs and

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<v Speaker 1>open them up, right, I mean this was this was

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<v Speaker 1>the days because you had to do this. Yeah, and

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<v Speaker 1>I hated it. I mean I absolutely And I was

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<v Speaker 1>always good at math and science, and I was like,

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<v Speaker 1>I do not like this course. And then I thought, man,

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<v Speaker 1>if I don't like biology, how am I going to

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<v Speaker 1>go to med? School, How am I gonna actually start

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<v Speaker 1>this whole process of being a doctor. But I had

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<v Speaker 1>a teacher who said, hey, why not think about engineering?

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<v Speaker 1>So what is it that you do now? I am

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<v Speaker 1>a roboticist, which means my research and my practice is

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<v Speaker 1>about building, designing, programming robots with the focus on the human,

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<v Speaker 1>improving the human quality of life, with an even more

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<v Speaker 1>focused initiative effort on children, so pediatrics. And you started

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<v Speaker 1>off designing rovers for NASA. So my very first job

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<v Speaker 1>as a roboticist, I was a robotics researcher and my

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<v Speaker 1>task was thinking about future rovers and how do we

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<v Speaker 1>enable them to navigate long range traversals on Mars. So

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<v Speaker 1>how did you go from rovers to the types of

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<v Speaker 1>robots that you're making today? You're really thinking about them

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<v Speaker 1>in a way of being assistive towards humans. Yeah, so

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<v Speaker 1>I think one of the things about my perception on

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<v Speaker 1>robotics systems is really the reason why I do it

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<v Speaker 1>is to assist us and improve our quality of life.

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<v Speaker 1>And you know, mind you, when I used to talk

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<v Speaker 1>about robotics, the first thing that people would say would

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<v Speaker 1>be like, oh, you're You're the one that is taking

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<v Speaker 1>over the jobs you're the one that took you know,

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<v Speaker 1>a lot of people in manufacturing. I had people say, yeah,

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<v Speaker 1>my grandfather got fired because of your robots, like that

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<v Speaker 1>was a common thing. And I understood that from very

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<v Speaker 1>very early on, and those were not the robots that

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<v Speaker 1>I wanted to build in design. But I think because

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<v Speaker 1>I always understood that I am part of an ecosystem,

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<v Speaker 1>I am part of a community, and my responsibility is

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<v Speaker 1>to become a contributing member of the community. And anything

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<v Speaker 1>I did, because my talent was my mind, anything I

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<v Speaker 1>did was about a overall positive net result versus a

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<v Speaker 1>negative you know. I was the kid that had to

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<v Speaker 1>go out into the neighborhood and paint over the graffiti

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<v Speaker 1>and pick up the like that was our Saturday. Like.

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<v Speaker 1>I didn't get to go out and play like the

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<v Speaker 1>other kids until I had done my chores, which was

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<v Speaker 1>you know, all of these community kind of things. And

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<v Speaker 1>so I think I just grew up realizing that it

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<v Speaker 1>was about community. But also my talent was my mind.

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<v Speaker 1>It was how I designed a thought about robotics. When

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<v Speaker 1>it comes to the types of robots you designed to

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<v Speaker 1>help people who have disabilities, is there anything personal that

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<v Speaker 1>happened to you that made you connect with them specifically.

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<v Speaker 1>I had always done these stems, so science, alogy, engineering,

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<v Speaker 1>and math camps primarily to engage girls and I'll say

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<v Speaker 1>underrepresented minority, since that's what we were called back in

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<v Speaker 1>the day. I had ran this one camp where there

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<v Speaker 1>was this young lady who had a visual impairment. She

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<v Speaker 1>was bright, bright, like smart, but the system did not

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<v Speaker 1>work for her because it wasn't accessible. And this was

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<v Speaker 1>the first time I'd heard this, were like, what is

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<v Speaker 1>accessible and what is assessively technology? Because I hadn't no idea,

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<v Speaker 1>And what it was is like I saw a problem.

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<v Speaker 1>I was like this, like makes no sense. Why is

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<v Speaker 1>our technology not able to be used by everyone? As

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<v Speaker 1>an engineer, you know, I see these kinds of problems

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<v Speaker 1>as basically a challenge to design a solution. It's just

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<v Speaker 1>the way I'm geared and think about it. And so

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<v Speaker 1>that started me down this rabbit hole of looking at

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<v Speaker 1>this target demographic understanding that we as engineers aren't really

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<v Speaker 1>addressing the needs of all the different populations. And I

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<v Speaker 1>was always a proponent of diversity, and I didn't really

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<v Speaker 1>understand until that moment that diversity includes raise includes ethnicity,

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<v Speaker 1>includes gender, but it also includes ability and disability. So

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<v Speaker 1>you have a number of apps, games, and toys all

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<v Speaker 1>under their company, zy Robotics. Is there a particular product

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<v Speaker 1>that you're like, you're especially proud of. I actually enjoy

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<v Speaker 1>all of them. I will say the one that I

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<v Speaker 1>especially like is one called Tommy the Turtle. Learn to Code.

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<v Speaker 1>Help your kids learn the basic of coding with Tommy

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<v Speaker 1>the Turtle. Your little one will absolutely love interacting with

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<v Speaker 1>Tommy and his colorful friends as they gain valuable skills

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<v Speaker 1>in programming. I can get a four year old to

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<v Speaker 1>learn how to code and love it. And you basically

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<v Speaker 1>have to do things like, you know, Tommy wants to

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<v Speaker 1>play with cat, but cats too far away. Can Tommy

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<v Speaker 1>move towards cats so they can play right? And so

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<v Speaker 1>there's a always these this you know, goofy rhyming and stuff,

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<v Speaker 1>and so then it'll go through like okay, in order

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<v Speaker 1>to do this, you know, you have to put in

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<v Speaker 1>this little button which is move one space right, and

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<v Speaker 1>so it walks you through this and at the end

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<v Speaker 1>it says, guess what you've done your first coding program.

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<v Speaker 1>Now let's try it with dog. And so it basically

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<v Speaker 1>builds up this sequential understanding sequences, understanding logic with the

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<v Speaker 1>subjective of again having friends, playing around and things like that.

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<v Speaker 1>And the Tommy to try to learn to code is

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<v Speaker 1>also accessible, so if you have a children within motor disability,

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<v Speaker 1>it actually has accessibility functions so you can use things

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<v Speaker 1>like switch devices. If you have slight visual impairments, there

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<v Speaker 1>are things you can do with the print. So I'm

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<v Speaker 1>most proud of that because I've actually seen like four

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<v Speaker 1>year olds like excited about coding, which is amazing and fascinating.

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<v Speaker 1>What types of benefits do you think children with disabilities

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<v Speaker 1>received from working with robots that you design? So one

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<v Speaker 1>of the things that we know is that with anything repetition, repetition,

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<v Speaker 1>repetition is good. The problem is is that the amount

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<v Speaker 1>of and I'll just call it exercise, which is repetition,

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<v Speaker 1>The amount of exercise you have to do has to

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<v Speaker 1>be done consistently, has to be done in a repeated fashion,

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<v Speaker 1>and typically you need someone to kind of guide you

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<v Speaker 1>because you know, they're kids, and we just don't have

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<v Speaker 1>enough resources. So most parents do not have are not

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<v Speaker 1>as privilege enough to bring in a human therapist into

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<v Speaker 1>the home every day to work with their child, And

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<v Speaker 1>so what we focused on was designing these robotic systems

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<v Speaker 1>that are adaptive so they have artificial intelligence in them

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<v Speaker 1>so that they can work with the kids basically every day.

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<v Speaker 1>So they are augmenting the services of a human therapist.

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<v Speaker 1>But in the home environment. Did you see an uptick

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<v Speaker 1>in people being interested in this type of technology during

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<v Speaker 1>COVID when you wasn't so easy to have like a

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<v Speaker 1>human aid in the house to help with children with disabilities? Yeah,

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<v Speaker 1>so I will say yes, and that goes across the

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<v Speaker 1>board in terms of AI, so artificial intelligence software as

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<v Speaker 1>well as robots. There was an uptick in the use

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<v Speaker 1>since March of twenty twenty, and it's primarily because of

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<v Speaker 1>the fact that you know, humans were dangerous, right, like

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<v Speaker 1>we know this. This was the whole thing is like

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<v Speaker 1>you're shut in. You're locked in because other humans who

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<v Speaker 1>are outside of your community, outside of your home are dangerous,

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<v Speaker 1>whereas robots were not. And so there was this whole

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<v Speaker 1>change and shift on the perception of robots, like robots

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<v Speaker 1>were more of like, oh, this actually enables me to

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<v Speaker 1>have a little bit of semblance of livelihood. And we

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<v Speaker 1>saw an uptick in companion robots because again, you couldn't

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<v Speaker 1>have you know, your young grandkids, for example, coming in,

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<v Speaker 1>or therapists necessarily come in, but robots could because they

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<v Speaker 1>were safer than people. There's folks that feel like our

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<v Speaker 1>addiction and technology is a problem, and building technology that

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<v Speaker 1>assists humans in whatever capacity kind of builds on that

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<v Speaker 1>addiction a little bit, or it feeds into that addiction. Rather,

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<v Speaker 1>what's your response to that to people that would would

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<v Speaker 1>say that maybe we need less technology and not more. Yeah,

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<v Speaker 1>So I actually have two answers to that. One is, yes,

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<v Speaker 1>it does feed on these addictions. Like my robots would

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<v Speaker 1>not work if I didn't model them based on humans

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<v Speaker 1>propensity to interact with these systems, right, it wouldn't work.

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<v Speaker 1>So we know that as a fact. But I would

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<v Speaker 1>say that that potential negative of being reliant on the

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<v Speaker 1>software on AI on robotis that the negatives are less

0:14:01.756 --> 0:14:05.836
<v Speaker 1>than the positives, and the positives are. It improves so

0:14:05.836 --> 0:14:10.236
<v Speaker 1>so many things. It makes access much more accessible in

0:14:10.396 --> 0:14:15.916
<v Speaker 1>terms of you know, equal opportunity about jobs, education, food sources,

0:14:15.916 --> 0:14:19.276
<v Speaker 1>and things like that. So the positives I think outweigh

0:14:19.356 --> 0:14:21.956
<v Speaker 1>the negatives over and over and over and over again.

0:14:23.196 --> 0:14:25.876
<v Speaker 1>When you see this type of interaction and you see,

0:14:26.796 --> 0:14:30.916
<v Speaker 1>you know, children interacted with the technology you've created and

0:14:30.916 --> 0:14:33.316
<v Speaker 1>the tools that you've created. How do you feel like,

0:14:33.396 --> 0:14:35.316
<v Speaker 1>how does that make you feel to see them interacting

0:14:35.316 --> 0:14:39.556
<v Speaker 1>with it successfully? My very first moment, I remember it

0:14:39.596 --> 0:14:41.836
<v Speaker 1>to this day. We were this is at Georgia Tech.

0:14:41.876 --> 0:14:44.996
<v Speaker 1>It was one of our very first studies. We had

0:14:45.036 --> 0:14:50.316
<v Speaker 1>a gamified therapy protocol called Superpop. We had to pop

0:14:50.356 --> 0:14:53.956
<v Speaker 1>these bubbles and it was linked to some movement therapy.

0:14:54.436 --> 0:14:56.036
<v Speaker 1>So I remember we go into the home and there

0:14:56.116 --> 0:14:59.516
<v Speaker 1>was this young child, child with cerebral palsy who was

0:14:59.516 --> 0:15:04.636
<v Speaker 1>in a wheelchair and severe sebesticity, so very limited movements.

0:15:05.036 --> 0:15:07.516
<v Speaker 1>And I remember the game because the game adapts to

0:15:07.676 --> 0:15:09.796
<v Speaker 1>the abilities of the child, and so you know, the

0:15:09.876 --> 0:15:12.276
<v Speaker 1>very first one, it started adapting and started adapting. And

0:15:12.316 --> 0:15:16.436
<v Speaker 1>I remember at one instance, this child touched the bubble

0:15:16.476 --> 0:15:19.516
<v Speaker 1>because it's popping bubbles, right, and I just I remember

0:15:19.716 --> 0:15:23.276
<v Speaker 1>like there was this smile. It was like the sun

0:15:23.516 --> 0:15:26.436
<v Speaker 1>like shone in this room. It was like all of

0:15:26.476 --> 0:15:28.716
<v Speaker 1>the wats that are out there, it just shone in

0:15:28.756 --> 0:15:31.956
<v Speaker 1>his face, pure joy, and it was like you you

0:15:31.956 --> 0:15:35.836
<v Speaker 1>couldn't have sold it. You couldn't have bottled it. You

0:15:35.876 --> 0:15:38.996
<v Speaker 1>couldn't have like bitcoined it right. It was like that

0:15:39.076 --> 0:15:42.756
<v Speaker 1>pure joy that only a child could show. And at

0:15:42.756 --> 0:15:44.996
<v Speaker 1>that point that was like my first moment going like,

0:15:45.116 --> 0:15:48.196
<v Speaker 1>oh my gosh, I feel really really really really good.

0:15:48.356 --> 0:15:51.036
<v Speaker 1>I like this feeling, I really do. It makes me

0:15:51.116 --> 0:15:54.956
<v Speaker 1>realize that I'm doing the right thing, impacting directly someone's life.

0:16:00.716 --> 0:16:04.716
<v Speaker 1>You're now working with children with disabilities, specifically in pediatrics,

0:16:04.836 --> 0:16:07.356
<v Speaker 1>and you know you said you got there because you

0:16:07.436 --> 0:16:10.796
<v Speaker 1>first wanted to work with with women, and being a

0:16:10.836 --> 0:16:14.196
<v Speaker 1>woman in STEM, I know, is not the easiest path

0:16:14.316 --> 0:16:17.276
<v Speaker 1>for most Do you find that you had any personal

0:16:17.316 --> 0:16:19.876
<v Speaker 1>experiences that kind of led you to say that I

0:16:19.916 --> 0:16:21.876
<v Speaker 1>want to kind of kick the ladder down for more

0:16:21.916 --> 0:16:26.076
<v Speaker 1>women to come into this field. Yes, when I started

0:16:26.636 --> 0:16:29.596
<v Speaker 1>at NASA and things like that, I was always passionate

0:16:29.636 --> 0:16:34.356
<v Speaker 1>about working in with young kids. Actually, even in undergrad

0:16:34.356 --> 0:16:37.316
<v Speaker 1>I tutored at the local high school and things like that.

0:16:37.676 --> 0:16:40.556
<v Speaker 1>So I'd always done that just because I remember growing up,

0:16:40.876 --> 0:16:43.116
<v Speaker 1>you know, engineers would come to my school and I

0:16:43.156 --> 0:16:45.036
<v Speaker 1>was inspired, and so I just felt that that was

0:16:45.076 --> 0:16:49.356
<v Speaker 1>a responsibility as an engineer. I didn't really feel that

0:16:49.636 --> 0:16:53.236
<v Speaker 1>I had a responsibility to be quote unquote, I guess

0:16:53.236 --> 0:16:57.196
<v Speaker 1>a role model until I was much older, and it

0:16:57.276 --> 0:17:01.156
<v Speaker 1>was because I suddenly realized that I was tired of

0:17:01.196 --> 0:17:06.076
<v Speaker 1>not seeing myself and therefore, like, I'm old, So it's

0:17:06.116 --> 0:17:08.756
<v Speaker 1>not like I can produce myself right now, that's my

0:17:08.836 --> 0:17:13.276
<v Speaker 1>age age, but I sure could start reaching and making

0:17:13.316 --> 0:17:17.756
<v Speaker 1>sure that the generation behind me saw themselves and didn't

0:17:17.756 --> 0:17:20.036
<v Speaker 1>have to go through the same things. And so it

0:17:20.116 --> 0:17:22.716
<v Speaker 1>then became more of a mission than you know, things

0:17:22.756 --> 0:17:24.396
<v Speaker 1>like oh I'm doing out right now here. No no, no no, no,

0:17:24.516 --> 0:17:26.996
<v Speaker 1>it's a mission because it sucks when you don't see yourself.

0:17:27.716 --> 0:17:31.676
<v Speaker 1>Are you speaking specifically about women? Are also specifically about

0:17:31.716 --> 0:17:36.636
<v Speaker 1>black women? So I actually have three. It's women marginalized community,

0:17:36.756 --> 0:17:40.316
<v Speaker 1>so black, Latin X, Indigenous, and it's also black women.

0:17:40.356 --> 0:17:44.316
<v Speaker 1>So it was actually three because those three there's not

0:17:44.396 --> 0:17:47.596
<v Speaker 1>a lot of us in any stages at the upper echelon,

0:17:47.676 --> 0:17:51.596
<v Speaker 1>it's not a lot. How do you think enfranchising those

0:17:51.596 --> 0:17:54.236
<v Speaker 1>groups that you're talking about, how do you think that's solved?

0:17:54.236 --> 0:17:57.156
<v Speaker 1>Do you think it's just solved with them just seeing

0:17:57.156 --> 0:17:59.076
<v Speaker 1>you in the room? Ward? What's the extra step you

0:17:59.076 --> 0:18:01.836
<v Speaker 1>feel that needs to be taken in order to draw

0:18:01.916 --> 0:18:04.756
<v Speaker 1>more of those people into the room. Yeah, so being

0:18:04.796 --> 0:18:07.836
<v Speaker 1>able to see yourself is only the first step, but

0:18:07.996 --> 0:18:10.236
<v Speaker 1>it is an important step because if you don't even

0:18:10.276 --> 0:18:12.316
<v Speaker 1>see yourself, you're not even going to think that it's

0:18:12.356 --> 0:18:14.516
<v Speaker 1>something for you. Right. So one is about, you know,

0:18:14.556 --> 0:18:18.556
<v Speaker 1>getting this motivation. The other is about ensuring that there's opportunities.

0:18:18.836 --> 0:18:22.156
<v Speaker 1>And so you know, if I see myself, but I

0:18:22.196 --> 0:18:25.876
<v Speaker 1>don't have access, I don't have the curriculum in the schools,

0:18:26.356 --> 0:18:28.316
<v Speaker 1>I don't know, you know, where am I supposed to

0:18:28.356 --> 0:18:30.796
<v Speaker 1>go in order to learn how to do artificial intelligence

0:18:30.876 --> 0:18:34.276
<v Speaker 1>or robotics? Then I might see myself, but I don't

0:18:34.276 --> 0:18:36.836
<v Speaker 1>have a path for it, right, And so it's about

0:18:36.876 --> 0:18:40.036
<v Speaker 1>seeing yourself, believing in that, and then having the opportunities

0:18:40.076 --> 0:18:44.756
<v Speaker 1>to excel. Irrespective of your environment or how you're growing up,

0:18:44.796 --> 0:18:54.516
<v Speaker 1>are those individuals who are around you. So there's a

0:18:54.516 --> 0:18:58.156
<v Speaker 1>lot of people that are afraid of a future with

0:18:58.316 --> 0:19:01.396
<v Speaker 1>robots at it. What would you say? How would you

0:19:02.036 --> 0:19:03.676
<v Speaker 1>talk to about and and you know what, I was

0:19:03.716 --> 0:19:05.796
<v Speaker 1>one of those people. I watched I robot and I

0:19:05.876 --> 0:19:08.676
<v Speaker 1>was like, no, no, not like that. That. What it's

0:19:08.716 --> 0:19:11.996
<v Speaker 1>easy to anything these days? What do you think the

0:19:12.036 --> 0:19:14.436
<v Speaker 1>future looks like with robots in it. As a roboticist,

0:19:14.476 --> 0:19:16.716
<v Speaker 1>what do you think that future? And actually, and I'm

0:19:16.756 --> 0:19:19.436
<v Speaker 1>sorry to take another step back, but those those Boston

0:19:19.516 --> 0:19:22.356
<v Speaker 1>Dynamics videos are really scary people when they see the

0:19:22.436 --> 0:19:24.676
<v Speaker 1>robots though in the backflips. And of course we've all

0:19:24.676 --> 0:19:27.316
<v Speaker 1>seen the Matrix. I'm now referencing all the movies. But

0:19:27.756 --> 0:19:29.476
<v Speaker 1>what would you say to people who have a fear

0:19:29.556 --> 0:19:33.556
<v Speaker 1>of the future with robots in it. The fear that

0:19:33.596 --> 0:19:39.116
<v Speaker 1>people have, honestly is the fear of the uncertainty, which

0:19:39.476 --> 0:19:42.196
<v Speaker 1>you're going to have irrespective of what it is. You know,

0:19:42.236 --> 0:19:44.396
<v Speaker 1>I think about you know, horse and carriage and the

0:19:44.396 --> 0:19:47.516
<v Speaker 1>cars came. I'm sure there were people like, oh, automobiles,

0:19:47.556 --> 0:19:50.356
<v Speaker 1>you know they kill people like people die in automobiles.

0:19:50.356 --> 0:19:52.756
<v Speaker 1>Why would you ever get it? Right? Like every single

0:19:52.876 --> 0:19:56.876
<v Speaker 1>instance where something has transformed our society for good, there's

0:19:56.916 --> 0:19:59.956
<v Speaker 1>always been this fear always always. We can go historically

0:19:59.956 --> 0:20:04.036
<v Speaker 1>when this has happened. And so robots in AI, artificial

0:20:04.036 --> 0:20:07.956
<v Speaker 1>intelligence is now that technology that is causing fear because

0:20:07.996 --> 0:20:12.076
<v Speaker 1>of the uncertainty. And this is happening and whether you

0:20:12.236 --> 0:20:15.116
<v Speaker 1>like it or not, whether you're afraid or not, it

0:20:15.196 --> 0:20:19.236
<v Speaker 1>is happening, and it will accelerate period guaranteed. And so

0:20:19.276 --> 0:20:22.996
<v Speaker 1>what I say is figure out how to become part

0:20:23.036 --> 0:20:26.316
<v Speaker 1>of the solution. If you're you know, fearful of the

0:20:26.436 --> 0:20:29.516
<v Speaker 1>I robots are feel full of, you know, bossing dynamics

0:20:29.596 --> 0:20:32.556
<v Speaker 1>kind of thing, well, become a computer scientist and go

0:20:32.636 --> 0:20:36.196
<v Speaker 1>work for those companies so that you don't have those

0:20:36.316 --> 0:20:39.076
<v Speaker 1>kind of robots being developed. Right, Like, this is like

0:20:39.316 --> 0:20:42.716
<v Speaker 1>part of the solution and also allows you to conquer

0:20:42.756 --> 0:20:46.356
<v Speaker 1>your fear but also take charge of your future. I

0:20:46.396 --> 0:20:48.476
<v Speaker 1>don't know why the idea of me taking years and

0:20:48.556 --> 0:20:50.756
<v Speaker 1>years of trigonometry just so I could go into box

0:20:50.796 --> 0:20:53.476
<v Speaker 1>of dynamics and be like, hey, what's going on in here?

0:20:53.596 --> 0:20:56.676
<v Speaker 1>What do you guys do these robots just sounded very,

0:20:56.796 --> 0:20:59.836
<v Speaker 1>very funny to me. But I love that. I think

0:20:59.836 --> 0:21:02.876
<v Speaker 1>that's a great solution. Well someone will hopefully someone's listening.

0:21:02.876 --> 0:21:06.356
<v Speaker 1>It's like, yeah, I love that idea. I'm doing it now.

0:21:12.836 --> 0:21:15.556
<v Speaker 1>Can we expand on the bias and racism forbid it?

0:21:15.996 --> 0:21:18.236
<v Speaker 1>Those stories do come to life, and I feel like

0:21:18.676 --> 0:21:20.836
<v Speaker 1>there has been some truth to some of them in

0:21:20.876 --> 0:21:23.076
<v Speaker 1>the research that I've done. Can you talk a little

0:21:23.116 --> 0:21:25.596
<v Speaker 1>bit about maybe dispel some of the some of the

0:21:25.636 --> 0:21:27.916
<v Speaker 1>fear that folks are having when it comes to bias

0:21:27.916 --> 0:21:31.316
<v Speaker 1>and racism from artificial intelligence and front robots, Like, what

0:21:31.676 --> 0:21:35.036
<v Speaker 1>exactly are we seeing there? Yeah, so there is there

0:21:35.116 --> 0:21:37.756
<v Speaker 1>is truth. So a lot of the systems that are

0:21:37.796 --> 0:21:42.196
<v Speaker 1>deployed that are out there have aspects of bias. And

0:21:42.716 --> 0:21:46.436
<v Speaker 1>what the definition of bias is that there are differences

0:21:46.476 --> 0:21:51.316
<v Speaker 1>in the results in their behaviors toward different types of

0:21:51.316 --> 0:21:57.996
<v Speaker 1>people based on gender, based on racial, ethnic identity, sexual orientation, religion,

0:21:58.236 --> 0:22:01.116
<v Speaker 1>like basically any type of attribute you can think about.

0:22:01.876 --> 0:22:06.316
<v Speaker 1>These systems have some aspect of bias. So that's the negative.

0:22:06.876 --> 0:22:11.636
<v Speaker 1>The positive, though, is that time and time and time again,

0:22:12.076 --> 0:22:15.236
<v Speaker 1>they're still better than the human biases. I think our

0:22:15.236 --> 0:22:17.836
<v Speaker 1>systems can be better. I honestly think we can design

0:22:17.956 --> 0:22:21.116
<v Speaker 1>and build robots that can make us better humans, that

0:22:21.156 --> 0:22:24.116
<v Speaker 1>can make us less biased humans. I totally believe that.

0:22:24.476 --> 0:22:27.476
<v Speaker 1>But unless we really focus on that, we're just going

0:22:27.556 --> 0:22:30.396
<v Speaker 1>to keep doing the same thing and propagate and just

0:22:30.476 --> 0:22:33.876
<v Speaker 1>continuing the biases that we have of the past. That's

0:22:33.996 --> 0:22:37.116
<v Speaker 1>what I'm afraid of. What do you say to the

0:22:37.156 --> 0:22:40.356
<v Speaker 1>activists who just heard this and heard you say that

0:22:40.596 --> 0:22:43.556
<v Speaker 1>robots are biased but less biased. How do you call

0:22:43.676 --> 0:22:46.316
<v Speaker 1>dib down to say, to get because I'm sure what

0:22:46.396 --> 0:22:47.876
<v Speaker 1>people are going to want to hear you say is

0:22:47.876 --> 0:22:49.996
<v Speaker 1>like they're not biased at all. Like we get there,

0:22:50.116 --> 0:22:52.156
<v Speaker 1>we get to a place where we're using these machines

0:22:52.436 --> 0:22:54.956
<v Speaker 1>and the data we're feeding into them and the way

0:22:54.956 --> 0:22:57.716
<v Speaker 1>that they're interacting with the world makes them get down

0:22:57.716 --> 0:23:00.516
<v Speaker 1>to zero bias. Is it even possible for that to

0:23:00.556 --> 0:23:03.556
<v Speaker 1>happen when they live in a world with biased humans

0:23:03.556 --> 0:23:06.196
<v Speaker 1>creating robots? Yeah? No. So I don't think that these

0:23:06.196 --> 0:23:09.116
<v Speaker 1>systems can ever get to zero bias because there will

0:23:09.196 --> 0:23:12.636
<v Speaker 1>always be a group that the system has not interacted with.

0:23:12.916 --> 0:23:15.436
<v Speaker 1>It might be that it's perfect, as perfect is perfect,

0:23:15.476 --> 0:23:21.436
<v Speaker 1>and then there's an unknown community in South Wales somewhere

0:23:21.916 --> 0:23:24.476
<v Speaker 1>that had never interacted, right, and now it doesn't work

0:23:24.476 --> 0:23:26.676
<v Speaker 1>with them, right. So I don't think that we can

0:23:26.716 --> 0:23:29.996
<v Speaker 1>ever get because as humans, we're unique and we're different,

0:23:30.076 --> 0:23:33.196
<v Speaker 1>Like there's an attribute that we're going to miss. So

0:23:33.236 --> 0:23:36.076
<v Speaker 1>I don't believe in zero bias. What I do believe, though,

0:23:36.236 --> 0:23:39.996
<v Speaker 1>is that because these systems are based on data and

0:23:40.036 --> 0:23:43.196
<v Speaker 1>they're not based on our lived experiences, it means that

0:23:43.276 --> 0:23:46.916
<v Speaker 1>you can basically quiz them. You can look at them, right,

0:23:46.956 --> 0:23:49.916
<v Speaker 1>So you can basically say, look, I have you know,

0:23:49.996 --> 0:23:54.516
<v Speaker 1>this person who is from a rural community that is

0:23:54.676 --> 0:23:58.596
<v Speaker 1>identifies as black. That's female, and I have the exact

0:23:58.636 --> 0:24:00.956
<v Speaker 1>same person. But now we change from female to male.

0:24:01.236 --> 0:24:03.516
<v Speaker 1>You know what's the outcome? Right, Like, you can ask

0:24:03.556 --> 0:24:05.956
<v Speaker 1>that and be like, oh wait, the differences are here, Okay,

0:24:06.036 --> 0:24:08.996
<v Speaker 1>we have some bias. Let's go fixes. Now you ask

0:24:09.356 --> 0:24:13.116
<v Speaker 1>human the same question, it'd be kind of hard to

0:24:13.156 --> 0:24:16.516
<v Speaker 1>figure out if they're biased or not, just in general.

0:24:16.556 --> 0:24:18.276
<v Speaker 1>And so that's what I'm saying is it's like you

0:24:18.316 --> 0:24:21.236
<v Speaker 1>can quiz the AI, and because it's based on data

0:24:21.276 --> 0:24:23.476
<v Speaker 1>and an algorithm, it can give you the answer you

0:24:23.476 --> 0:24:26.156
<v Speaker 1>ask a human and if they know they're biased, they're

0:24:26.156 --> 0:24:28.916
<v Speaker 1>gonna lie. And if they don't know they're biased, they're

0:24:28.956 --> 0:24:31.356
<v Speaker 1>just going to make up excuses because they don't realize

0:24:31.356 --> 0:24:34.956
<v Speaker 1>that they are putting their own lived experience in their decision.

0:24:35.796 --> 0:24:38.156
<v Speaker 1>So you would say the difference between robot bias and

0:24:38.236 --> 0:24:42.276
<v Speaker 1>human bias is the ability to be I guess, radically

0:24:42.276 --> 0:24:44.636
<v Speaker 1>transparent in the fact that we can open them up,

0:24:44.676 --> 0:24:47.556
<v Speaker 1>look at their code and see what the answer is. Correct.

0:24:47.596 --> 0:24:49.596
<v Speaker 1>Although we don't do that now. And that's why a

0:24:49.636 --> 0:24:52.156
<v Speaker 1>lot of this stuff is going on about AI and

0:24:52.196 --> 0:24:56.036
<v Speaker 1>biases because companies that are putting these out are not

0:24:56.396 --> 0:24:58.556
<v Speaker 1>opening it up. They're not doing the assessment, they're not

0:24:58.596 --> 0:25:02.356
<v Speaker 1>doing the analysis. It's other researchers like myself that are

0:25:02.476 --> 0:25:04.756
<v Speaker 1>you know, third party looking at and be like, hey,

0:25:05.076 --> 0:25:07.956
<v Speaker 1>there's something going on here, right it shouldn't be our

0:25:08.076 --> 0:25:10.596
<v Speaker 1>role and our responsibile. But that's what we're doing for

0:25:10.636 --> 0:25:13.876
<v Speaker 1>the community because the companies are not doing it for themselves.

0:25:19.836 --> 0:25:23.116
<v Speaker 1>What do you think our listeners can do to kind

0:25:23.116 --> 0:25:26.756
<v Speaker 1>of support this mission and improve the marketplace and kind

0:25:26.796 --> 0:25:30.596
<v Speaker 1>of help more with learn more about AI and assistive technologies,

0:25:30.796 --> 0:25:33.516
<v Speaker 1>but kind of also engage with it more. One thing

0:25:33.556 --> 0:25:36.796
<v Speaker 1>that I think everyone has to figure out is how

0:25:36.836 --> 0:25:40.476
<v Speaker 1>to code and how to program. Honestly, the jobs are changing,

0:25:40.716 --> 0:25:43.756
<v Speaker 1>and even the jobs you think are, you know, well,

0:25:43.756 --> 0:25:47.236
<v Speaker 1>what about law, what about policing? What about Guess what?

0:25:47.556 --> 0:25:49.236
<v Speaker 1>If you don't know how to code, there's going to

0:25:49.276 --> 0:25:51.356
<v Speaker 1>be a whole next generation that's going to come take

0:25:51.396 --> 0:25:53.356
<v Speaker 1>your job. It's going to be humans that take it.

0:25:53.436 --> 0:25:55.316
<v Speaker 1>But it's because they know how to do the new

0:25:55.356 --> 0:25:57.356
<v Speaker 1>types of jobs that are going to be out there,

0:25:57.756 --> 0:25:59.756
<v Speaker 1>So that's one thing everyone needs to learn how to

0:25:59.756 --> 0:26:03.076
<v Speaker 1>program and the code, not be a computer scientist, but

0:26:03.436 --> 0:26:06.116
<v Speaker 1>learn that as a skill set. Second is is I

0:26:06.156 --> 0:26:09.756
<v Speaker 1>think people need to do a little bit more investigation

0:26:10.116 --> 0:26:12.156
<v Speaker 1>of the things that are out there instead of just

0:26:12.316 --> 0:26:15.756
<v Speaker 1>listening to media, because a lot of things that are

0:26:15.836 --> 0:26:19.356
<v Speaker 1>out are the horror stories. So what types of publications

0:26:19.476 --> 0:26:21.676
<v Speaker 1>or media sources do you think that listeners can go

0:26:21.756 --> 0:26:26.916
<v Speaker 1>to in order to get accurate information about STEM, about robotics,

0:26:27.156 --> 0:26:31.476
<v Speaker 1>about science and technology generally. So I actually personally like Wired.

0:26:31.756 --> 0:26:37.356
<v Speaker 1>It's a technology magazine, but it is really accessible to

0:26:37.436 --> 0:26:39.796
<v Speaker 1>like a general audience, and it shows both sides. So

0:26:39.836 --> 0:26:43.396
<v Speaker 1>it has stories that you talk about the negatives of AI,

0:26:43.516 --> 0:26:46.396
<v Speaker 1>but it also in the same article we'll talk about

0:26:46.396 --> 0:26:49.956
<v Speaker 1>the positive. So I think in terms of being accessible,

0:26:50.076 --> 0:26:54.396
<v Speaker 1>that's one of my favorite Doctor Howard, thank you so

0:26:54.476 --> 0:27:06.076
<v Speaker 1>much for being with us. Thank you. Doctor Ayana Howard

0:27:06.156 --> 0:27:08.716
<v Speaker 1>is a roboticist and dean of the College of Engineering

0:27:08.836 --> 0:27:12.676
<v Speaker 1>at the Ohio State University. Be sure to check out

0:27:12.716 --> 0:27:14.356
<v Speaker 1>our show notes to find out ways you can learn

0:27:14.356 --> 0:27:17.996
<v Speaker 1>more about robotics and how to get involved. Next week,

0:27:17.996 --> 0:27:20.836
<v Speaker 1>we're talking about vaccine passes and how they might help

0:27:20.876 --> 0:27:23.396
<v Speaker 1>solve the problem of safe reentry and to some of

0:27:23.396 --> 0:27:29.316
<v Speaker 1>our busiest cities. Solvable Senior Producer is Jocelyn Frank, Research

0:27:29.396 --> 0:27:33.876
<v Speaker 1>by David Jah, Booking by Lisa Dunn. Our managing producer

0:27:33.916 --> 0:27:37.356
<v Speaker 1>is Sasha Matthias, and our executive producer is Mio Lobel.

0:27:38.556 --> 0:27:44.276
<v Speaker 1>Special thanks to Heather Fame, John Schnars, Carl Migliori, Christina Sullivan,

0:27:44.596 --> 0:27:50.276
<v Speaker 1>Eric Sandler, Maggie Taylor, Emily Rostick, Maya Kanik, and Kadijah Holland.

0:27:51.436 --> 0:27:54.436
<v Speaker 1>Solvable is a production of Pushkin Industries. If you like

0:27:54.556 --> 0:27:57.916
<v Speaker 1>the show, please remember to share, rate, and review. It

0:27:57.996 --> 0:28:00.716
<v Speaker 1>helps us find our way to the ears of new listeners.

0:28:01.676 --> 0:28:04.876
<v Speaker 1>You can find Pushkin podcasts wherever you listen, including on

0:28:04.916 --> 0:28:09.796
<v Speaker 1>the iHeartRadio app and Apple Podcasts. I'm Ronald Young Junior.

0:28:10.316 --> 0:28:24.036
<v Speaker 1>Thanks for listening. M